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The study systematically assesses algorithmic fairness in machine learning models for predicting treatment retention in medication for opioid use disorder, finding performance gaps across patient subgroups and evaluating bias mitigation techniques with trade-offs.
This research evaluates the enhancement of opioid use disorder prediction by integrating patient-reported survey data with electronic health records, demonstrating improved performance across multiple machine learning models.
This paper compares five feature selection methods for EHR diagnosis codes in opioid use disorder prediction, finding that NTK sensitivity offers the best accuracy-stability balance while LLM-guided selection adds complementary clinical signals.